ABSTRACT Resting state networks are a fascinating yet poorly understood phenomenon. Sets of spatially separated regions show correlated slow fluctuations in fMRI BOLD signals, most obvious when subjects are at rest. These networks appear to have clinical imporantance: brain injuries perturb resting state networks, and multiple clinical disorders, including depression, dyslexia and prosopagnosia, are associated with specific resting state network abnormalities. Resting state data are used to infer functional connections between regions, but little is known about how neuronal activity gives rise to these networks. Furthermore, despite much speculation, little is known about how resting network state might influence task-related neuronal activity. Understanding the reciprocal relationships between resting state networks and neural activity has the potential to revolutionize our understanding of brain function. The reason that a gap in our knowledge exists is primarily due to the fact that it is difficult to characterize resting state networks without being within an active MRI scanner, and difficult to record spikes from neurons in such an environment (and even more difficult to record from multiple cells at once in such an environment). Alternative methods most involve serial recording of resting state networks and neuronal activity, recording of lower frequency electrical signals (LFP), or the use of optical methods in mouse which provide a close but not exact surrogate of neuronal activity (e.g., calcium signals) and access only to the uppermost layers of cortex. We propose to develop an innovative method to address this issue: high density parallel recording and oxygen polarography using carbon fiber microwires widely dispersed across the cortex of an awake behaving non-human primate. We have already demonstrated long-range correlations using oxygen polarography recorded on standard size micro-electrodes, paired with standard unit-recording electrodes. These correlations resemble resting state phenomenon, but in order to capture and relate the dynamics of neural activity to the dynamics of resting state networks, much denser spatial sampling is required.